Wojciech Łukowski^*

BIOCELL, Vol.50, No.10, 2026, DOI:10.32604/biocell.2026.081112 - 22 September 2026

Abstract Type 1 diabetes (T1D) is traditionally described as a T cell–mediated autoimmune disease, yet accumulating longitudinal evidence indicates that metabolic and environmental perturbations—including depletion of short-chain fatty acid (SCFA)–producing gut microbiota—precede seroconversion and overt autoimmunity. We propose that SCFA loss represents an upstream trigger of endocannabinoid system (ECS) imbalance in T1D. Integrating evidence from microbiome, lipid signaling, mitochondrial biology, and immunometabolic research, we construct a mechanistic model in which reduced SCFA availability impairs lipid homeostasis and promotes overproduction of 2-arachidonoylglycerol (2-AG), potentially driving cannabinoid receptor 1 (CB1) dominance and receptor asymmetry. The resulting arachidonic acid More >

Leonardo Daniel Villalana Alvarez¹, Abraham Heriberto García-Campoy², Alethia Muñiz-Ramirez^3,*

Phyton-International Journal of Experimental Botany, Vol.95, No.8, 2026, DOI:10.32604/phyton.2026.085201 - 28 August 2026

Abstract Diabetes mellitus is a metabolic disorder characterized by chronic hyperglycemia, oxidative stress, and the formation of advanced glycation end products (AGEs). This study examined the antiglycation, antioxidant, and antidiabetic properties of the methanolic extract of Ibervillea sonorae (IsM). Antiglycation activity was assessed in vitro using a bovine serum albumin–glucose model, and the antidiabetic effects were evaluated using a zebrafish model of experimentally induced hyperglycemia. Gas chromatography–mass spectrometry (GC–MS) analysis revealed several metabolites, including fatty acids and phytosterols. The extract significantly inhibited AGEs formation in vitro and reduced AGEs accumulation in the ocular tissue of diabetic zebrafish. In vivo treatment More > Graphic Abstract

Dominick Shoha^#, David Lei^#, Tyler Truong, Sophia Strukel, Elliot Enshaie, Vikrant Rai^*

BIOCELL, Vol.50, No.8, 2026, DOI:10.32604/biocell.2026.078846 - 27 July 2026

Abstract Diabetes, inflammation, and neurodegeneration, particularly Alzheimer’s disease (AD), are deeply interconnected (brain diabetes). Type 2 diabetes mellitus (T2DM) acts as a significant risk factor for neurodegenerative diseases like Alzheimer’s (AD) and Parkinson’s (PD) by inducing chronic inflammation, oxidative stress, and metabolic dysfunction. Hyperglycemia drives neuroinflammation and damages the blood-brain barrier (BBB), exacerbating cognitive decline and neuronal loss. Chronic inflammation acts as a central bridge, linking high blood sugar, insulin resistance, and metabolic dysfunction in the brain to the buildup of amyloid plaques, tau tangles, and neuronal damage due to shared insulin signaling issues in the More >

Bailing Zhang^*, Genlang Chen

Journal of Intelligent Medicine and Healthcare, Vol.4, pp. 109-124, 2026, DOI:10.32604/jimh.2026.084876 - 21 July 2026

Abstract High-stakes clinical decision support (CDS) demands a property that aggregate accuracy cannot capture: a trace that a clinician who was not in the room can inspect layer by layer when the system is wrong. We argue that the way to obtain this property is to refuse to entangle the large language model (LLM) with the rest of the pipeline. We propose KRD (Knowledge–Rule–Decision), a four-component architecture that separates fact extraction, a compile-time clinical knowledge layer in the spirit of the LLM Wiki pattern of Karpathy, a rule layer of hand-written contraindications and heuristics, and a decision… More >

Wafa Alagal^1,*, Zanyar A. Ameen^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.083259 - 30 June 2026

Abstract Diabetes remains a major global health challenge and requires diagnostic systems capable of handling uncertainty and sometimes conflicting clinical evidence. In this study, a Disc T-Spherical Fuzzy (DT-SF) TOPSIS framework is proposed for diabetes risk assessment, where the radius parameter is used to encode the confidence associated with each diagnostic attribute. The methodology also integrates the Analytic Hierarchy Process (AHP) to determine the relative importance of several key risk factors, including blood glucose, body mass index, family history, lifestyle factors, and clinical symptoms. One important feature of the proposed approach is the ternary classification scheme,… More >

Jianlin Xie^1,#, Jingde Wu¹, Qingwei Zhang¹, Yuanqi Guo¹, Xiande Huang^2,*

Canadian Journal of Urology, Vol.33, No.3, pp. 723-728, 2026, DOI:10.32604/cju.2026.075856 - 29 June 2026

Abstract Background: Congenital nephrogenic diabetes insipidus (CNDI) is most frequently caused by mutations in the AVPR2 gene. Patients exhibit persistent polyuria due to renal insensitivity to antidiuretic hormone. Chronic high urine output predisposes to bladder dysfunction and upper urinary-tract dilatation, notably hydronephrosis. Although pharmacotherapy can partially reduce urine volume, its capacity to reverse established hydronephrosis is limited. Clean intermittent catheterization (CIC), a mainstay in managing neurogenic bladder, warrants investigation regarding its utility in CNDI-associated hydronephrosis.
Case Description: A 9-year-old Chinese boy presented with lifelong polydipsia and polyuria, with a peak 24-h urine output of approximately 7100 mL. Renal ultrasonography… More >

Nomangwane Angelina Tshabalala¹, Ping Guo^2,*

Journal on Internet of Things, Vol.8, pp. 87-107, 2026, DOI:10.32604/jiot.2026.078248 - 06 May 2026

Abstract Type 2 Diabetes mellitus is a disease that afflicts approximately 537 million individuals all over the world, and continuous glucose monitoring (CGM) systems have become very important in the management of the disease. Nonetheless, the existing centralized data architecture of CGM generates high privacy and security risks, as sensitive patient health data can be easily abused. This paper introduces an original structure that incorporates both federated learning and blockchain technology and allows for predicting glucose safely and preserving privacy without affecting the integrity of the data. Our model uses the Long Short-Term Memory (LSTM) neural… More >

Shuyu Yuan^1,#, Guoxiao Han^1,#, Huimin Qiu¹, Henan Zheng², Rongzhi Fang¹, Wangmiao Xie¹, Wangui Yu^1,*, Xiaochun Peng^1,*

BIOCELL, Vol.50, No.4, 2026, DOI:10.32604/biocell.2026.075338 - 21 April 2026

Abstract The gut microbiota plays a pivotal role in maintaining host metabolic homeostasis. Accumulating evidence has demonstrated that dysbiosis of the gut microbiota is closely associated with metabolic disorders, including obesity, type 2 diabetes mellitus (T2DM), and non-alcoholic fatty liver disease (NAFLD). These alterations affect energy harvest, bile acid and short-chain fatty acid metabolism, intestinal barrier integrity, and low-grade inflammation, thereby contributing to insulin resistance and ectopic fat accumulation. In this narrative review, we summarize current knowledge on microbiome-host interactions in metabolic diseases, with a focus on energy metabolism, immune regulation, and inflammatory pathways. We further More > Graphic Abstract

Jiafei He^1,2, Ailixiati Alifu², Haifan Wang², Renwei Chen^1,2,*

Structural and Congenital Heart Disease, Vol.21, No.1, 2026, DOI:10.32604/schd.2026.075858 - 31 March 2026

Abstract Hyperglycemia in pregnancy (HIP) is an important independent risk factor for congenital heart disease (CHD) in offspring. With an increasing number of women of childbearing age experiencing gestational hyperglycemia, the impact of an intrauterine hyperglycemic environment on fetal development has drawn significant attention. However, the teratogenic mechanisms underlying its effects on cardiac development remain incompletely understood. This review systematically analyzes relevant literature to summarize its underlying mechanisms and key findings: A hyperglycemic environment disrupts cardiac neural crest cell migration, differentiation, and the proliferation/apoptosis balance of cardiomyocytes by inducing oxidative stress, endoplasmic reticulum stress, and inflammatory… More >

Abdullahi Abubakar Imam^1,*, Sahalu Balarabe Junaidu², Hussaini Mamman³, Ganesh Kumar³, Abdullateef Oluwagbemiga Balogun³, Sunder Ali Khowaja⁴, Shuib Basri³, Luiz Fernando Capretz⁵, Asmah Husaini⁶, Hanif Abdul Rahman⁶, Usman Ali¹, Fatoumatta Conteh¹

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.076187 - 30 March 2026

Abstract Artificial Intelligence (AI) in healthcare enables predicting diabetes using data-driven methods instead of the traditional ways of screening the disease, which include hemoglobin A1c (HbA1c), oral glucose tolerance test (OGTT), and fasting plasma glucose (FPG) screening techniques, which are invasive and limited in scale. Machine learning (ML) and deep neural network (DNN) models that use large datasets to learn the complex, nonlinear feature interactions, but the conventional ML algorithms are data sensitive and often show unstable predictive accuracy. Conversely, DNN models are more robust, though the ability to reach a high accuracy rate consistently on… More >